OPTIMIZATION AND RESILIENCY IN THE RENEWABLE ENERGY SECTOR
OPTIMIZATION AND RESILIENCY IN THE RENEWABLE ENERGY SECTOR
STAT-EI’s technology delivers consistent power savings—otherwise known as energy optimization. We achieve these savings by using machine trained artificial intelligence algorithms. Our savings range from 3% when installed in a renewable energy-only generation facility to 12% when renewable energy and energy storage are paired.
We program our smart state of charge system and smart controller to fit the users’ needs and predict future energy generation requirements with 95% accuracy. We pre-load network
configurations, apply machine learning algorithms that continue to learn and evolve, and can model or load major network changes to plan physical network updates (for instance, as population changes)—so the network operation is always optimized. Our solutions provide:
- Grid frequency response control
- Congestion management using localized/distributed energy reserves
- Short-term network management/sizing to meet changing load growth patterns
- Industrial client (large energy user) energy management
1. Artificial intelligence learning, integration, and rerouting
Our artificial intelligence learns a network’s ‘day-to-day’ operations (particularly sub-grids and key high-demand customers), including operation under different environmental conditions. It integrates the day-to-day data with available weather inputs (e.g., sensors, satellite feeds, and forecasts), to identify network failures and help reroute network resources as needed. In addition, our artificial intelligence can address common congestion areas via localized storage banks.
Versions of our system also include algorithmic trading software to participate in key energy trading markets including:
PJM (Eastern, USA)
ERCOT (Texas, USA)
CAISO (California, USA)
UK ESO (Europe)
IESO (Ontario, Canada)
2. Instant Network Response Adjustments
System operators rely on generators and turbines’ physical aspects to provide inertia and frequency control to the grid network. These physical characteristics are an embedded part of running the network and aren’t missed until they are gone.
As renewable (particularly solar) grid penetration increases it will reduce the number of physical generators synchronized to the network, and impact network control and response. Additionally,
sudden load shifts—on or off—will exacerbate network challenges.
STAT-EI’s advanced artificial intelligence algorithms monitor a range of parameters for each device and learn network usage patterns and storage characteristics. Our algorithms integrate with large battery farms and/or dispersed network batteries to appropriately store energy and deliver frequency response control to the network. Using our algorithms lowers cost and significantly lowers greenhouse gas emissions compared to thermal generation. Our technology manages load shifts in near real time to provide frequency response and maintain voltage levels within specifications.
3. Manage Line Congestion with Localized Energy Reserves
During peak demand, transmission and distribution capacity can be a bottleneck in the network which forces system operators to dispatch expensive local generation. Historically this has taken the form of dirty ‘peaker’ plants or additional transmission lines that are only used for a couple of hours or days.
STAT-EI’s artificial intelligence helps operators combine properly controlled energy storage (with or without local renewable energy) and mitigate congestion and inject power where it is needed—downstream of transmission constraints. Energy storage can charge during off-peak hours and discharge as needed to serve congested load pockets during peak demand. As population pressures change (and the network morphs), operators can redeploy assets to respond to new network challenges and our artificial intelligence immediately adapts.
4. Size Your Network to Meet Changing Load Growth Patterns
Traditional ‘pole-and-wire’ projects are major capital investments, that require years of advance planning to accommodate future load forecasts. They are traditionally initially oversized and underutilized to accommodate load forecasts years in advance. In most networks, the top 10% of peak demand typically occurs for less than 1% of the yeas. As a result, T&D assets often are only half-used for the majority of the time.
By deploying local storage (with or without local renewable energy) and STAT-EI’s artificial intelligence tools, our systems help incremental scale local energy system investments to deliver local load relief as needed. When a new line is commissioned, existing storage arrays can be re-sited to support other local load pocket requirements. Our artificial intelligence adapts immediately, allowing the network to scale and adapt in a measured and controlled way.
5. Transform Your Energy Use, Manage Your Energy Costs, and Protect Your Facilities From Outages
Energy pricing fluctuations and rising demand charges can significantly impact large power consumer’s bottom line. Commercial and industrial clients can use energy storage to charge during off-peak times and discharge during peak to flatten their load profile and manage energy costs. By controlling “when” your facility pulls power from the grid, you can reduce demand charges, avoid costly peak rates, and save up to 30% on your average electricity bill.
Storage also insulates your facilities from outages, and protects you from potential revenue losses and damaged equipment. STAT-EI’s artificial intelligence tools manage your storage assets so in the event of a blackout, you can meet your facility’s demand with on-site energy storage to continuously operate and minimize negative impacts on production, equipment, and revenue.
When local renewable energy is added to the mix, industrial (and other high power) users have the greatest number of options at hand. STAT-EI’s artificial intelligence oversees all of these options to manage and monitor power demand, manage renewable energy and grid storage charging, and minimize power costs. In the event of power outages, our artificial intelligence increases available localized renewable energy options to help fuel the microgrid.